Recently, wireless communication techniques for providing various multimedia services in wireless communication environments are being developed. In order to provide high quality multimedia services, data transmission at a high speed is required. As a representative technique for supporting data transmission at a high speed, research on a Multiple Input Multiple Output (MIMO) technique is actively ongoing.
According to the MIMO technique, a plurality of antennas are used to increase channel capacity within limited frequency resources. In an environment where scattering is rich, a plurality of antennas are used to provide channel capacity theoretically proportional to the number of antennas.
According to the MIMO technique, a space and area where antennas can be installed are limited, and a spacing between antennas much influences on communication performance. That is, the narrower the spacing between antennas is, the higher a correlation between radio channels is. Especially, when antennas have the same polarization, radio channels have a very high correlation with each other. The radio channels having a high correlation are not completely independent from each other. Accordingly, the high correlation may prevent increase of a sum data rate of a communication system using the MIMO technique. Furthermore, interference occurring between the radio channels may reduce the reliability of data communication, and may reduce a data transmission rate.
In order to efficiently transmit data according to the MIMO technique, data has to be coded in advance, which is referred to as ‘pre-coding’. And, a rule for data pre-coding is expressed as a matrix, which is referred to as ‘pre-coding matrix’. The pre-coding matrix is generated based on one or more codeword matrices included in a codebook.
A base station performs data pre-coding based on information about a channel status between itself and a terminal, and transmits the pre-coded data to the terminal. Then, the terminal measures a channel between itself and the base station, and feedbacks information to the base station on the measured channel.
The conventional closed-loop MIMO technique may enhance the efficiency of a MIMO system by exchanging feedback information between a transmitter and a receiver through a reverse direction channel.
The feedback information exchanged in the conventional closed-loop MIMO technique includes channel status information, transmission signal process vector information, etc.
Especially, a closed-loop MIMO beam-forming technique for obtaining an array gain based on a diversity gain and multi-transmission antennas has been applied to a communication system standard such as 3GPP release-99, 3GPP LTE, and IEEE 802.16e, by using vectors which process beam-forming transmission signals based on a codebook.
Methods for generating a codebook may include a Grassmannian packing method, a Lloyd-type vector quantization method, a discrete Fourier transform (DFT) method, etc. Especially, the methods for generating a codebook may include a Grassmannian based Householder method in IEEE 802.16e, and may include a DFT based Householder method in 3GPP LTE.
The Grassmannian packing method indicates a method for selecting vectors equally spaced from each other in a domain based on a characteristic that optimal beam-forming vectors are isotropically distributed in the domain where beam-forming signal process vectors exist, and for configuring a codebook based on the selected vectors.
The Lloyd-type vector quantization method indicates a method for quantizing beam-forming vectors into representative vectors that can minimize a preset expectation value of a distortion function with consideration of a randomly distributed characteristic of a channel matrix.
The DFT method indicates a method for using a Fourier transform matrix having a unitary characteristic as a codebook.
The Householder method indicates a method for generating a matrix having a unitary characteristic, the matrix which enables a multi-stream or multi-user closed-loop MIMO system by using preset beam-forming vectors.
FIG. 1 shows a MIMO system in accordance with the conventional art.
As shown in FIG. 1, a transmitter 20 is provided with NT antennas, and a receiver 10 is provided with NR antennas.
NR×1 reception signal vectors received from the transmitter 20 having NT antennas by the receiver 10 having NR antennas may be expressed as the following Equation 1.r=Hx+n  Equation 1
Here, H denotes a channel matrix of NR×NT, X denotes a NT×1 transmission signal vector, and n denotes additive white Gaussian noise (AWGN) of NR×1.
The channel matrix (H) may be expressed as the following Equation 2 by a singular value decomposition (SVD).H=UΣVH  Equation 2
Here, Σ=diag{σ1, σ2, . . . , σrank(H)} denotes a diagonal matrix composed of Eigen values of H, and U=[u1, u2, . . . , urank(H)] and V=[v1, v2, . . . , vrank(H)] are matrices composed of Eigen vectors corresponding to the Eigen values.
Accordingly, an index of a maximum Eigen value is defined as
  m  =                              arg          ⁢                                          ⁢          max          ⁢                      {                          σ              i                        }                                                                    i            =            1                    ,          …          ⁢                                          ,                      rank            ⁢                          {              H              }                                ,                    and a method for using vm and umH as transmission and reception beam-forming vectors is defined as a maximum Eigen-mode transmission method.
However, the conventional beam-forming method has the following problems.
Firstly, the base station has to precisely know the channel status information with the terminal.
Secondly, the transmission beam-forming vector (vm) has to be informed to the base station by a large amount of feedback resources.
As a method for obtaining a beam-forming gain with utilizing only limited feedback resources, there have been proposed codebook-based beam-forming methods. According to the proposed methods, a transmitter and a receiver share a predetermined codebook, and the receiver feedbacks, to the transmitter, only an index indicating beam-forming vectors inside the codebook. Here, in the case of using feedback resources having B bits, 2B beam-forming vectors may be included in the codebook.
However, the codebook-based closed-loop MIMO beam-forming method also utilizes limited number of feedback bits. This may cause a codebook set to be configured, the codebook set having elements corresponding to the number of beam-forming vector indexes that can be indicated by the limited number of feedback bits.
Due to the restrictions that the limited number of bits are used, optimal beam-forming vectors having a random characteristic can not be precisely represented. Especially, the more the number of transmission antennas is, the more performance degradation due to the limited number of feedback bits increases.
Furthermore, beam-forming vectors generated based on feedback information selected by the terminal may be outdated due to a user's mobility, feedback delay, etc. This may result in performance degradation which becomes more severe as the user's mobility is greater and the feedback delay is longer.